Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations.

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance.

Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method.

In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects.

Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting.

Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network.

A Versatile and Shelf-Stable Dielectric Coupling Medium for Microwave Imaging.

Objective: To develop a new class of emulsions using a protein-based emulsifier as the coupling fluid for microwave imaging systems.

Methods: In this paper, we provide a theoretical basis for engineering shelf-stable dielectric fluids, a step-by-step formulation method, and measurements of complex dielectric properties in the frequency range of 0.5-3 GHz, which can be applicable for many of the recent microwave imaging systems.

Wearable magnetic induction-based approach toward 3D motion tracking.

Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms.

Author Correction: Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.

Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints.

Electromagnetic Imaging of Dielectric Objects Using a Multi-Directional Search-Based Simulated Annealing.

In this paper, we introduce a global optimization method that is a novel combination of the simulated annealing method and the multi-directional search algorithm. We demonstrate the use of the algorithm for a microwave-imaging system to obtain the electrical properties of objects. The proposed global optimizer significantly improves the performance and speed of the simulated annealing method by utilizing a nonlinear simplex search, starting from an initial guess, and taking effective steps in obtaining the global solution of the minimization problem.

Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar.

The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of sub-grid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here we compare 3-arc-sec (~100-m) spatial resolution AirMOSS RZSM retrievals from P-band radar acquisitions over 9 separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Hydrology Model (Flux-PIHM).

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska.

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modelling and analysis framework combining field observations, local scale (~ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from airborne low frequency (L+P-band) radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modelled ALT results show good correspondence with in situ measurements in higher permafrost probability (PP ≥ 70%) areas (n = 33, R = 0.

Data Assimilation to extract Soil Moisture Information from SMAP Observations.

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture.

Real-Time Three-Dimensional Microwave Monitoring of Interstitial Thermal Therapy.

We report a method for real-time three-dimensional monitoring of thermal therapy through the use of noncontact microwave imaging. This method is predicated on using microwaves to image changes in the dielectric properties of tissue with changing temperature. Instead of the precomputed linear Born approximation that was used in prior work to speed up the frame-to-frame inversions, here we use the nonlinear distorted Born iterative method (DBIM) to solve the electric volume integral equation (VIE) to image the temperature change.

Real-time microwave imaging of differential temperature for thermal therapy monitoring.

A microwave imaging system for real-time 3-D imaging of differential temperature has been developed for the monitoring and feedback of thermal therapy systems. Design parameters are constrained by features of a prototype-focused microwave thermal therapy system for the breast, operating at 915 MHz. Real-time imaging is accomplished with a precomputed linear inverse scattering solution combined with continuous vector network analyzer (VNA) measurements of a 36-antenna, HFSS-modeled, cylindrical cavity.

Self-characterization of commercial ultrasound probes in transmission acoustic inverse scattering: transducer model and volume integral formulation.

A self-contained source characterization method for commercial ultrasound probes in transmission acoustic inverse scattering is derived and experimentally tested. The method is based on modified scattered field volume integral equations that are linked to the source-scattering transducer model. The source-scattering parameters are estimated via pair-wise transducer measurements and the nonlinear inversion of an acoustic propagation model that is derived.

A preclinical system prototype for focused microwave thermal therapy of the breast.

A preclinical prototype of a transcutaneous thermal therapy system has been developed for the targeted treatment of breast cancer cells using focused microwaves as an adjuvant to radiation, chemotherapy, and high-intensity-focused ultrasound. The prototype system employs a 2-D array of tapered microstrip patch antennas operating at 915 MHz to focus continuous-wave microwave energy transcutaneously into the pendent breast suspended in a coupling medium. Prior imaging studies are used to ascertain the material properties of the breast tissue, and these data are incorporated into a multiphysics model.

Microwave breast imaging system prototype with integrated numerical characterization.

The increasing number of experimental microwave breast imaging systems and the need to properly model them have motivated our development of an integrated numerical characterization technique. We use Ansoft HFSS and a formalism we developed previously to numerically characterize an S-parameter- based breast imaging system and link it to an inverse scattering algorithm. We show successful reconstructions of simple test objects using synthetic and experimental data.

Large domain, low-contrast acoustic inverse scattering for ultrasound breast imaging.

We present a full-wave acoustic inverse scattering algorithm designed specifically for ultrasonic breast imaging. At ultrasonic frequencies, the image domain is roughly tens to hundreds of min cubed, where min is the smallest wavelength in the transmit signal spectrum. The expected range of contrasts for the breast imaging problem for density, compressibility, and compressive loss is ± 20% of the background.